Note

Click here to download the full example code

RecoBundles80 using AFQ API

An example using the AFQ API to run recobundles with the 80 bundle atlas.

import os.path as op

import plotly

from AFQ import api
import AFQ.data as afd

Get some example data

Retrieves Stanford HARDI dataset.

afd.organize_stanford_data(clear_previous_afq=True)

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Set tractography parameters (optional)

We make this tracking_params which we will pass to the AFQ object which specifies that we want 50,000 seeds randomly distributed in the white matter.

We only do this to make this example faster and consume less space.

tracking_params = dict(n_seeds=50000,
                       random_seeds=True,
                       rng_seed=42)

Initialize an AFQ object:

We specify seg_algo as reco80 in segmentation_params. This tells the AFQ object to perform RecoBundles using the 80 bundles atlas in the segmentation step.

myafq = api.AFQ(bids_path=op.join(afd.afq_home,
                                  'stanford_hardi'),
                preproc_pipeline='vistasoft',
                segmentation_params={"seg_algo": "reco80"},
                tracking_params=tracking_params)

Out:

Downloading https://templateflow.s3.amazonaws.com/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz

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Visualizing bundles and tract profiles:

This would run the script and visualize the bundles using the plotly interactive visualization, which should automatically open in a new browser window.

bundle_html = myafq.all_bundles_figure
plotly.io.show(bundle_html["01"])

Out:

Optimizing level 2 [max iter: 10000]
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 65%|######5   | 12736/19503 [00:10<00:04, 1355.60 MB/s]
 66%|######6   | 12917/19503 [00:10<00:04, 1352.01 MB/s]
 67%|######7   | 13101/19503 [00:10<00:04, 1355.98 MB/s]
 68%|######8   | 13290/19503 [00:10<00:04, 1368.64 MB/s]
 69%|######9   | 13468/19503 [00:10<00:04, 1354.77 MB/s]
 70%|######9   | 13652/19503 [00:11<00:04, 1358.11 MB/s]
 71%|#######   | 13832/19503 [00:11<00:04, 1351.37 MB/s]
 72%|#######1  | 14020/19503 [00:11<00:04, 1364.22 MB/s]
 73%|#######2  | 14201/19503 [00:11<00:03, 1356.79 MB/s]
 74%|#######3  | 14387/19503 [00:11<00:03, 1362.67 MB/s]
 75%|#######4  | 14570/19503 [00:11<00:03, 1357.33 MB/s]
 76%|#######5  | 14751/19503 [00:11<00:03, 1349.41 MB/s]
 76%|#######6  | 14919/19503 [00:12<00:03, 1316.83 MB/s]
 77%|#######7  | 15100/19503 [00:12<00:03, 1324.75 MB/s]
 78%|#######8  | 15291/19503 [00:12<00:03, 1350.83 MB/s]
 79%|#######9  | 15479/19503 [00:12<00:02, 1362.81 MB/s]
 80%|########  | 15666/19503 [00:12<00:02, 1369.32 MB/s]
 81%|########1 | 15849/19503 [00:12<00:02, 1363.20 MB/s]
 82%|########2 | 16031/19503 [00:12<00:02, 1357.23 MB/s]
 83%|########3 | 16217/19503 [00:12<00:02, 1364.64 MB/s]
 84%|########4 | 16405/19503 [00:13<00:02, 1373.72 MB/s]
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100%|##########| 19503/19503 [00:15<00:00, 1274.45 MB/s]

Total running time of the script: ( 46 minutes 47.756 seconds)

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